Senior Data Labeling Jobs in Atlanta

Rex.zone is hiring for senior data labeling jobs in Atlanta (Remote, Full-Time). This role is a search-recognizable data labeling and AI/ML evaluation position focused on training data quality, annotation guidelines compliance, and large language model evaluation. You will contribute to LLM training pipelines through RLHF, prompt evaluation, QA evaluation, and content safety labeling across NLP and computer vision annotation tasks. If you have hands-on experience improving model performance with high-precision labeled datasets, apply to Rex.zone to support AI labs, tech startups, and annotation vendors with scalable, reliable data operations.

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Job Overview

keyword: Senior Data Labeling Jobs in Atlanta | job_title: Senior Data Labeling Specialist (Atlanta) Date: 25-02-2026 | Company: Rex.zone | Country: US | Remote Type: Remote | Employment Type: FULL_TIME | Experience Level: Mid-Senior | Industry: Technology | Job Function: Engineering | Skills: data labeling, RLHF, LLM evaluation, QA evaluation, prompt evaluation, annotation guidelines, named entity recognition, computer vision annotation, content safety labeling, training data quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR You will lead and execute high-accuracy data labeling and evaluation work that directly supports AI/ML training workflows, including LLM training pipelines, RLHF preference ranking, and multi-stage QA evaluation. This position emphasizes end-to-end training data quality: interpreting annotation guidelines, resolving edge cases, calibrating annotators, and producing consistent labels for downstream model performance improvement.

What You Will Work On

You will deliver labeled datasets and evaluation signals used to train, fine-tune, and validate models across NLP, computer vision, and content safety domains. Work includes prompt evaluation, response ranking for RLHF, named entity recognition (NER) and entity linking, taxonomy-driven content safety labeling, and image/video annotation. You will partner with data ops and engineering stakeholders to translate model requirements into annotation guidelines compliance and measurable quality targets.

Core Responsibilities

You will own consistent labeling outputs and quality standards across projects, focusing on training data quality and evaluation rigor. Responsibilities include: executing complex annotation tasks; performing QA evaluation (spot checks, audits, inter-annotator agreement); writing and refining annotation guidelines; handling ambiguous cases with documented rationales; supporting rubric design for large language model evaluation; and providing feedback loops that drive model performance improvement.

Required Qualifications

Mid-Senior experience in data labeling, data annotation, or AI/ML evaluation with demonstrated accuracy and throughput. Strong understanding of annotation guidelines compliance, error taxonomy, and quality measurement (e.g., agreement scoring, sampling strategies). Familiarity with NLP concepts (NER, intent/slot labeling, classification), LLM evaluation (rubrics, prompt evaluation, preference ranking), and/or computer vision annotation (bounding boxes, polygons, segmentation). Comfort working in remote, metrics-driven production environments.

Preferred Qualifications

Experience with RLHF workflows (pairwise ranking, preference data, reasoning trace evaluation where applicable), content safety labeling policies, multilingual evaluation, and prompt-based testing for LLM behavior. Prior work with AI labs, tech startups, BPOs, or annotation vendors. Ability to mentor peers, drive calibration sessions, and improve SOPs for scalable labeling operations.

Tools, Data Types, and Workflow

You will work with structured and unstructured datasets including text, conversation logs, and image/video samples. Typical workflows include task ingestion, guideline review, calibration, annotation, QA evaluation, disagreement resolution, and delivery with clear documentation. You will apply consistent rubrics for large language model evaluation and maintain traceability for decisions that affect training data quality.

Compensation and Employment Details

keyword: Senior Data Labeling Jobs in Atlanta | job_title: Senior Data Labeling Specialist (Atlanta) Date: 25-02-2026 | Company: Rex.zone | Country: US | Remote Type: Remote | Employment Type: FULL_TIME | Experience Level: Mid-Senior | Industry: Technology | Job Function: Engineering | Skills: data labeling, RLHF, LLM evaluation, QA evaluation, prompt evaluation, annotation guidelines, named entity recognition, computer vision annotation, content safety labeling, training data quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR This is a Remote, Full-Time role aligned to senior data labeling work supporting production AI/ML pipelines. Compensation range is USD 63360 to USD 126720 per year, depending on scope, domain complexity (NLP, computer vision, content safety), and demonstrated evaluation/QA leadership.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your role-relevant background (data labeling, QA evaluation, RLHF or prompt evaluation exposure) and examples of guideline-driven work. Highlight experience improving training data quality, handling edge cases, and delivering consistent outputs that support model performance improvement. Candidates may be asked to complete a short calibration-style assessment aligned with large language model evaluation or annotation guidelines compliance.

Frequently Asked Questions

  • Q: Are these senior data labeling jobs in Atlanta remote?

    Yes. The role is explicitly marked Remote while targeting candidates in the Atlanta market for “senior data labeling jobs Atlanta” search intent.

  • Q: Is this a full-time role or contract/freelance?

    This posting is for FULL_TIME employment. Rex.zone may also list contract or freelance data labeling roles separately, but this page is for full-time senior hiring.

  • Q: What does “senior” mean for a data labeling role?

    Senior scope typically includes complex annotation, consistent guideline application, ownership of training data quality, QA evaluation oversight, calibration support, and contributions to rubric design for large language model evaluation.

  • Q: What kinds of tasks are included (NLP, computer vision, content safety)?

    Depending on project needs, tasks can include NLP labeling (classification, NER), RLHF preference ranking and prompt evaluation for LLMs, computer vision annotation (bounding boxes/segmentation), and content safety labeling using policy-driven taxonomies.

  • Q: Do I need prior RLHF experience?

    RLHF experience is helpful but not always required. Strong data labeling fundamentals, annotation guidelines compliance, and QA evaluation discipline are essential, and you can ramp into RLHF workflows through calibrated rubrics and feedback.

  • Q: How is quality measured in this role?

    Quality is measured through QA evaluation processes such as sampling audits, inter-annotator agreement, error categorization, guideline adherence, and consistency checks tied to training data quality and model performance improvement goals.

  • Q: What skills should I include to match this job?

    Include skills aligned to the posting intent: data labeling, RLHF, LLM evaluation, QA evaluation, prompt evaluation, annotation guidelines, named entity recognition, computer vision annotation, content safety labeling, and training data quality.

  • Q: Which employer types does this work support?

    The work supports AI labs, tech startups, BPOs, and annotation vendors by delivering reliable labeled data and evaluation signals for production AI/ML and LLM training pipelines.

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